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| import torch | |
| import torch.utils.data | |
| from librosa.filters import mel as librosa_mel_fn | |
| MAX_WAV_VALUE = 32768.0 | |
| def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): | |
| """ | |
| PARAMS | |
| ------ | |
| C: compression factor | |
| """ | |
| return torch.log(torch.clamp(x, min=clip_val) * C) | |
| def dynamic_range_decompression_torch(x, C=1): | |
| """ | |
| PARAMS | |
| ------ | |
| C: compression factor used to compress | |
| """ | |
| return torch.exp(x) / C | |
| def spectral_normalize_torch(magnitudes): | |
| output = dynamic_range_compression_torch(magnitudes) | |
| return output | |
| def spectral_de_normalize_torch(magnitudes): | |
| output = dynamic_range_decompression_torch(magnitudes) | |
| return output | |
| mel_basis = {} | |
| hann_window = {} | |
| def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): | |
| if torch.min(y) < -1.1: | |
| print("min value is ", torch.min(y)) | |
| if torch.max(y) > 1.1: | |
| print("max value is ", torch.max(y)) | |
| global hann_window | |
| dtype_device = str(y.dtype) + "_" + str(y.device) | |
| wnsize_dtype_device = str(win_size) + "_" + dtype_device | |
| if wnsize_dtype_device not in hann_window: | |
| hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to( | |
| dtype=y.dtype, device=y.device | |
| ) | |
| y = torch.nn.functional.pad( | |
| y.unsqueeze(1), | |
| (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), | |
| mode="reflect", | |
| ) | |
| y = y.squeeze(1) | |
| spec = torch.stft( | |
| y, | |
| n_fft, | |
| hop_length=hop_size, | |
| win_length=win_size, | |
| window=hann_window[wnsize_dtype_device], | |
| center=center, | |
| pad_mode="reflect", | |
| normalized=False, | |
| onesided=True, | |
| return_complex=False, | |
| ) | |
| spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) | |
| return spec | |
| def spectrogram_torch_conv(y, n_fft, sampling_rate, hop_size, win_size, center=False): | |
| # if torch.min(y) < -1.: | |
| # print('min value is ', torch.min(y)) | |
| # if torch.max(y) > 1.: | |
| # print('max value is ', torch.max(y)) | |
| global hann_window | |
| dtype_device = str(y.dtype) + '_' + str(y.device) | |
| wnsize_dtype_device = str(win_size) + '_' + dtype_device | |
| if wnsize_dtype_device not in hann_window: | |
| hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) | |
| y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') | |
| # ******************** original ************************# | |
| # y = y.squeeze(1) | |
| # spec1 = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], | |
| # center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) | |
| # ******************** ConvSTFT ************************# | |
| freq_cutoff = n_fft // 2 + 1 | |
| fourier_basis = torch.view_as_real(torch.fft.fft(torch.eye(n_fft))) | |
| forward_basis = fourier_basis[:freq_cutoff].permute(2, 0, 1).reshape(-1, 1, fourier_basis.shape[1]) | |
| forward_basis = forward_basis * torch.as_tensor(librosa.util.pad_center(torch.hann_window(win_size), size=n_fft)).float() | |
| import torch.nn.functional as F | |
| # if center: | |
| # signal = F.pad(y[:, None, None, :], (n_fft // 2, n_fft // 2, 0, 0), mode = 'reflect').squeeze(1) | |
| assert center is False | |
| forward_transform_squared = F.conv1d(y, forward_basis.to(y.device), stride = hop_size) | |
| spec2 = torch.stack([forward_transform_squared[:, :freq_cutoff, :], forward_transform_squared[:, freq_cutoff:, :]], dim = -1) | |
| # ******************** Verification ************************# | |
| spec1 = torch.stft(y.squeeze(1), n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], | |
| center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) | |
| assert torch.allclose(spec1, spec2, atol=1e-4) | |
| spec = torch.sqrt(spec2.pow(2).sum(-1) + 1e-6) | |
| return spec | |
| def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): | |
| global mel_basis | |
| dtype_device = str(spec.dtype) + "_" + str(spec.device) | |
| fmax_dtype_device = str(fmax) + "_" + dtype_device | |
| if fmax_dtype_device not in mel_basis: | |
| mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) | |
| mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to( | |
| dtype=spec.dtype, device=spec.device | |
| ) | |
| spec = torch.matmul(mel_basis[fmax_dtype_device], spec) | |
| spec = spectral_normalize_torch(spec) | |
| return spec | |
| def mel_spectrogram_torch( | |
| y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False | |
| ): | |
| if torch.min(y) < -1.0: | |
| print("min value is ", torch.min(y)) | |
| if torch.max(y) > 1.0: | |
| print("max value is ", torch.max(y)) | |
| global mel_basis, hann_window | |
| dtype_device = str(y.dtype) + "_" + str(y.device) | |
| fmax_dtype_device = str(fmax) + "_" + dtype_device | |
| wnsize_dtype_device = str(win_size) + "_" + dtype_device | |
| if fmax_dtype_device not in mel_basis: | |
| mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) | |
| mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to( | |
| dtype=y.dtype, device=y.device | |
| ) | |
| if wnsize_dtype_device not in hann_window: | |
| hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to( | |
| dtype=y.dtype, device=y.device | |
| ) | |
| y = torch.nn.functional.pad( | |
| y.unsqueeze(1), | |
| (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), | |
| mode="reflect", | |
| ) | |
| y = y.squeeze(1) | |
| spec = torch.stft( | |
| y, | |
| n_fft, | |
| hop_length=hop_size, | |
| win_length=win_size, | |
| window=hann_window[wnsize_dtype_device], | |
| center=center, | |
| pad_mode="reflect", | |
| normalized=False, | |
| onesided=True, | |
| return_complex=False, | |
| ) | |
| spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) | |
| spec = torch.matmul(mel_basis[fmax_dtype_device], spec) | |
| spec = spectral_normalize_torch(spec) | |
| return spec |